Patrolling monocytes inhibit osteosarcoma metastasis to the lung

Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning

Objectives: Osteosarcoma is the most common primary malignant tumor in children and adolescents, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past decades. Effective biomarkers in diagnosing osteosarcoma are warranted to be developed. This study aims to explore novel biomarkers correlated with immune cell infiltration in the development and diagnosis of osteosarcoma. Methods: Three datasets (GSE19276, GSE36001, GSE126209) comprising osteosarcoma samples were extracted from Gene Expression Omnibus (GEO) database and merged to obtain the gene expression. Then, differentially expressed genes (DEGs) were identified by limma and potential biological functions and downstream pathways enrichment analysis of DEGs was performed. The machine learning algorithms LASSO regression model and SVM-RFE (support vector machine-recursive feature elimination) analysis were employed to identify candidate hub genes for diagnosing patients with osteosarcoma. Receiver operating characteristic (ROC) curves were developed to evaluate the discriminatory abilities of these candidates in both training and test sets. Furthermore, the characteristics of immune cell infiltration in osteosarcoma, and the correlations between these potential genes and immune cell abundance were illustrated using CIBERSORT. qRT-PCR and western blots were conducted to validate the expression of diagnostic candidates. Results: GEO datasets were divided into the training (merged GSE19276, GSE36001) and test (GSE126209) groups. A total of 71 DEGs were screened out in the training set, including 10 upregulated genes and 61 downregulated genes. These DEGs were primarily enriched in immune-related biological functions and signaling pathways. After machine learning by SVM-RFE and LASSO regression model, four biomarkers were chosen for the diagnostic nomogram for osteosarcoma, including ASNS, CD70, SRGN, and TRIB3. These diagnostic biomarkers all possessed high diagnostic values (AUC ranging from 0.900 to 0.955). Furthermore, these genes were significantly correlated with the infiltration of several immune cells, such as monocytes, macrophages M0, and neutrophils. Conclusion: Four immune-related candidate hub genes (ASNS, CD70, SRGN, TRIB3) with high diagnostic value were confirmed for osteosarcoma patients. These diagnostic genes were significantly connected with the immune cell abundance, suggesting their critical roles in the osteosarcoma tumor immune microenvironment. Our study provides highlights on novel diagnostic candidate genes with high accuracy for diagnosing osteosarcoma patients.

Global scientific trends on matrix metalloproteinase and osteosarcoma: A bibliometric and visualized analysis

Objective

This study aimed to identify author, country, institutional, and journal collaborations and their impacts, assess the knowledge base, identify existing trends, and uncover emerging topics related to the role of Metalloproteinase in osteosarcoma.

Methods

945 Articles and reviews associated with the role of Metalloproteinase in osteosarcoma were obtained from the WoSCC and analyzed by Citespace and Vosviewer.

Results

The main aspects of research on the role of MMP in OS are invasion and metastasis. The latest hotspots were found to be the mechanism of MMP promoting invasion and metastasis, lung metastasis, and antitumor activity. Notably, invasion, metastasis, and antitumor activity were potentially turning points in the MMP-OS field. In the future, the primary research hotspot in the field of MMP-OS may be to study the mechanism, explore their role in the OS lung metastasis, and determine their role in the cancer therapy process.

Conclusion

This study thus offers a comprehensive overview of the MMP-OS-related field using bibliometrics and visual methods, which will provide a valuable reference for researchers interested in the field of MMP-OS.

Genome-wide identification of alternative splicing and splicing regulated in immune infiltration in osteosarcoma patients

Background: Osteosarcoma typically occurs in adolescents, and the survival rate of patients with metastatic and recurrent osteosarcoma remains low. Abnormal regulation of alternative splicing is associated with the development of osteosarcoma. However, there is no genome-wide analysis of the function and regulatory mechanisms of aberrant alternative splicing associated with osteosarcoma. Methods: Published transcriptome data on osteosarcoma (GSE126209) derived from osteosarcoma patient tissue were downloaded. Gene expression profiling by high-throughput sequencing was performed on 9 normal samples and 10 tumor samples for genome-wide identification of osteosarcoma-related alternative splicing events. The potential function of osteosarcoma-associated alternative splicing events was examined by immune infiltration and correlation analysis. Regulation of aberrantly expressed RNA-binding proteins (RBPs) related to alternative splicing in osteosarcoma was clarified by co-expression analysis. Results: A total of 63 alternative splicing events, which are highly credible and dominant, were identified. GO enrichment analysis indicated that alternative splicing may be closely related to the immune response process. Immune infiltration analysis showed significant changes in the percentages of CD8 T cells, resting memory CD4 T cells, activated memory CD4 T cells, monocytes, resting dendritic cells, and activated mast cells in tumors compared to normal tissues, indicating the involvement of these immune cell types in the occurrence of osteosarcoma. Moreover, the analysis identified alternative splicing events that were co-altered with resting memory CD4 T cells, resting dendritic cells, and activated mast cells, events that may be associated with regulation of the osteosarcoma immune microenvironment. In addition, a co-regulatory network (RBP-RAS-immune) of osteosarcoma-associated RBPs with aberrant alternative splicing and altered immune cells was established. These RBPs include NOP58, FAM120C, DYNC1H1, TRAP1, and LMNA, which may serve as molecular targets for osteosarcoma immune regulation. Conclusion: These findings allow us to further understand the causes of osteosarcoma development and provide a new research direction for osteosarcoma immunotherapy or targeted therapy.

RNA adenosine modifications related to prognosis and immune infiltration in osteosarcoma

Background

RNA adenosine modifications, which are primarily mediated by “writer” enzymes (RMWs), play a key role in epigenetic regulation in various biological processes, including tumorigenesis. However, the expression and prognostic role of these genes in osteosarcoma (OS) remain unclear.

Methods

Univariate and multivariate Cox analyses were used to construct the RMW signature for OS using Target datasets. RMW expression in OS tissue was detected by qPCR analysis. Xcell and GSVA were used to determine the relationship between RMWs and immune infiltration. The DGIdb and CMap databases were used for drug prediction. In vivo and in vitro experiments showed that strophanthidin elicited antitumor activity against OS.

Results

A 3-RMW (CSTF2, ADAR and WTAP) prognostic signature in OS was constructed using the Target dataset and verified using GEO datasets and 63 independent OS tissues via qPCR analysis. High-risk OS patients had poor overall survival, and the prognostic signature was an independent prognostic factor for OS. Functional studies showed that tumour-, metabolism-, cell cycle- and immune-related pathways were related to high risk. Next, we found that RMW-derived high-risk patients exhibited increased infiltration of M2 macrophages and cDCs. Furthermore, we predicted the potential drugs for OS using the DGIdb and CMap databases. In vivo and in vitro experiments showed that strophanthidin elicited antitumor activity against OS by repressing cell growth and inducing cell cycle arrest at the G1 phase.

Conclusion

The 3-RWM-based prognostic signature established in this study is a novel gene signature associated with immune infiltration, and strophanthidin was identified as a candidate therapy for OS by repressing OS cell growth and the cell cycle.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03415-6.

N6-Methyladenosine-Related lncRNAs Are Potential Prognostic Biomarkers and Correlated With Tumor Immune Microenvironment in Osteosarcoma

N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) play vital roles in the prognostic value and immune microenvironment of malignant tumors. Here, we constructed a m6A-related lncRNA signature in osteosarcoma samples from TCGA dataset and analyzed the association of the signature with tumor immune microenvironment. m6A-related lncRNAs were identified by performing Pearson's correlation analysis and were used to construct a novel m6A-related lncRNA signature in osteosarcoma. Validation in testing and entire cohorts confirmed the satisfactory accuracy of the risk signature. Principal-component analysis verifies the grouping ability of the risk signature. Functional enrichment analyses connected immune with the risk signature based on the six m6A-related lncRNAs. When patients were separated into high- and low-risk group based on their risk scores, we found that patients in the high-risk group had lower stromal scores, immune scores, and ESTIMATE scores, while the tumor purity was higher in the high-risk group than that in the low-risk group. As for immune cell infiltration, the proportion of monocytes was significantly higher in the low-risk group than that in the high-risk group. Of the six lncRNAs, AC004812.2 was a protective factor in osteosarcoma and low expression of AC004812.2 predicted worse overall survival. Overexpression of AC004812.2 inhibited 143B cell proliferation and increased the expression levels of IGF2BP1 and YTHDF1. In all, our m6A-related lncRNA signature was a potential prognostic biomarker and correlated with tumor immune microenvironment and immune cell infiltration, and AC004812.2 might be an important regulator of m6A modification and a promising therapeutic target in osteosarcoma.

The Heterogeneity of Infiltrating Macrophages in Metastatic Osteosarcoma and Its Correlation with Immunotherapy

Background

Metastatic osteosarcoma is a common and fatal bone tumor. Several studies have found that tumor-infiltrating immune cells play pivotal roles in the progression of metastatic osteosarcoma. However, the heterogeneity of infiltrating immune cells across metastatic and primary osteosarcoma remains unclear.

Methods

Immune infiltration analysis was carried out via the “ESTIMATE” and “xCell” algorithms in primary and metastatic osteosarcoma. Then, we evaluated the prognostic value of infiltrating immune cells in 85 osteosarcomas through the Kaplan–Meier (K-M) and receiver operating characteristic (ROC) curve. Infiltrations of macrophage M1 and M2 were evaluated in metastatic osteosarcoma, as well as their correlation with immune checkpoints. Macrophage-related prognostic genes were identified through Weighted Gene Coexpression Network Analysis (WGCNA), Lasso analysis, and Random Forest algorithm. Finally, a macrophage-related risk model had been constructed and validated.

Results

Macrophages, especially the macrophage M1, sparingly infiltrated in metastatic compared with the primary osteosarcoma and predicted the worse overall survival (OS) and disease-free survival (DFS). Macrophage M1 was positively correlated with immune checkpoints PDCD1, CD274 (PD-L1), PDCD1LG2, CTLA4, and TIGIT. In addition, four macrophage-related prognostic genes (IL10, VAV1, CD14, and CCL2) had been identified, and the macrophage-related risk model had been validated to be reliable for evaluating prognosis in osteosarcoma. Simultaneously, the risk score showed a strong correlation with several immune checkpoints.

Conclusion

Macrophages potentially contribute to the regulation of osteosarcoma metastasis. It can be used as a candidate marker for metastatic osteosarcoma' prognosis and immune checkpoints blockades (ICBs) therapy. We constructed a macrophage-related risk model through machine-learning, which might help us evaluate patients' prognosis and response to ICBs therapy.

CT45A1 promotes the metastasis of osteosarcoma cells in vitro and in vivo through β-catenin

Increased expression of cancer/testis antigens (CTAs) is reported in various tumors. However, the unique role of CTAs in tumor genesis has not yet been verified. Here, we first report the functional role of CT45A1 in the carcinogenesis of osteosarcoma. RNA sequencing and immunohistochemistry confirmed that elevated expression of CT45A1 was detected in osteosarcoma, especially in metastatic tissues of osteosarcoma. Furthermore, osteosarcoma patients with poorer prognosis showed high expression of CT45A1. In cell tests, CT45A1 overexpression was shown to strengthen the proliferation, migration, and invasion abilities of osteosarcoma cells, while silencing CT45A1 markedly elicited the opposite effects in these tests by disrupting the activation of β-catenin. In summary, we identify a novel role of CT45A1 in osteosarcoma. Furthermore, our results suggested that CT45A1 may contribute to the development of osteosarcoma and could be a possible therapeutic target for osteosarcoma patients.

Immune classification of osteosarcoma

Tumor immune microenvironment has been shown to be important in predicting the tumor progression and the outcome of treatments. This work aims to identify different immune patterns in osteosarcoma and their clinical characteristics. We use the latest and best performing deconvolution method, CIBERSORTx, to obtain the relative abundance of 22 immune cells. Then we cluster patients based on their estimated immune abundance and study the characteristics of these clusters, along with the relationship between immune infiltration and outcome of patients. We find that abundance of CD8 T cells, NK cells and M1 Macrophages have a positive association with prognosis, while abundance of γδ T cells, Mast cells, M0 Macrophages and Dendritic cells have a negative association with prognosis. Accordingly, the cluster with the lowest proportion of CD8 T cells, M1 Macrophages and highest proportion of M0 Macrophages has the worst outcome among clusters. By grouping patients with similar immune patterns, we are also able to suggest treatments that are specific to the tumor microenvironment.